Global imaging processing algorithms may be used in agriculture to capture (e.g., record) images that typically do not produce a standard color image. Hyperspectral and/or multispectral imaging may also be used in agriculture, but conventional hyperspectral and/or multispectral images may typically be captured from relatively high elevations so that simple analysis may detect chlorophyll levels in plants. Normalized Difference Vegetation Index (NDVI) may also be used to automate produce farming. Although NDVI may give a farmer a high level overview of the health status of crops, NDVI may be mostly based on low level features (e.g., colors, edges, etc.). NDVI may therefore result in several ambiguities such as, for example, incorrectly detecting damage caused by mole burrowing as a stockpile. In addition, while drones may be in use today in agriculture, the analysis may be largely based on simple near infrared (IR) image processing techniques, which produce maps that fail to provide agriculture details needed to distinguish between different types of damage.